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YOLOv11改进-Neck篇-给yolov11分别增加小目标检测层P2和大目标检测层P6

一、本文介绍

本文给大家带来的最新改机制是给YOLOv11分别增加 小目标P2 和 大目标检测层P6 （两个yaml文件），有的读者可能检测的目标是小目标，那么我们增加小 目标检测 层是非常容易涨点的，或者你的检测目标是图片占比很大的（很少有这样的）那么增加大目标检测层是非常好的选择，本文根据YOLOv8官方提供的P2和P6两个版本yaml文件，进行YOLOv11的参考性移植，大家可以选择P2或者P6作为自己的一个论文创新点.

P2版本的训练信息：YOLO11-P2 summary: 379 layers, 2,669,988 parameters, 2,669,972 gradients, 10.4 GFLOPs
P6版本的训练信息：YOLO11-P6 summary: 385 layers, 3,691,148 parameters, 3,691,132 gradients, 5.1 GFLOPs

欢迎大家订阅我的专栏一起学习YOLO！

二、yaml文件

2.1 增加 小目标检测 层P2的yaml文件

此版本的训练信息：YOLO11-P2 summary: 379 layers, 2,669,988 parameters, 2,669,972 gradients, 10.4 GFLOPs

注意：其中的小目标检测层P2的False,大家可以尝试设置为True尝试效果. 

  1. # Ultralytics YOLO 🚀, AGPL-3.0 license
  2. # YOLO11 object detection model with P3-P5 outputs. For Usage examples see https://docs.ultralytics.com/tasks/detect
  4. # Parameters
  5. nc: 80 # number of classes
  6. scales: # model compound scaling constants, i.e. 'model=yolo11n.yaml' will call yolo11.yaml with scale 'n'
  7.   # [depth, width, max_channels]
  8.   n: [0.50, 0.25, 1024] # summary: 319 layers, 2624080 parameters, 2624064 gradients, 6.6 GFLOPs
  9.   s: [0.50, 0.50, 1024] # summary: 319 layers, 9458752 parameters, 9458736 gradients, 21.7 GFLOPs
  10.   m: [0.50, 1.00, 512] # summary: 409 layers, 20114688 parameters, 20114672 gradients, 68.5 GFLOPs
  11.   l: [1.00, 1.00, 512] # summary: 631 layers, 25372160 parameters, 25372144 gradients, 87.6 GFLOPs
  12.   x: [1.00, 1.50, 512] # summary: 631 layers, 56966176 parameters, 56966160 gradients, 196.0 GFLOPs
  14. # YOLO11n backbone
  15. backbone:
  16.   # [from, repeats, module, args]
  17.   - [-1, 1, Conv, [64, 3, 2]] # 0-P1/2
  18.   - [-1, 1, Conv, [128, 3, 2]] # 1-P2/4
  19.   - [-1, 2, C3k2, [256, False, 0.25]]
  20.   - [-1, 1, Conv, [256, 3, 2]] # 3-P3/8
  21.   - [-1, 2, C3k2, [512, False, 0.25]]
  22.   - [-1, 1, Conv, [512, 3, 2]] # 5-P4/16
  23.   - [-1, 2, C3k2, [512, True]]
  24.   - [-1, 1, Conv, [1024, 3, 2]] # 7-P5/32
  25.   - [-1, 2, C3k2, [1024, True]]
  26.   - [-1, 1, SPPF, [1024, 5]] # 9
  27.   - [-1, 2, C2PSA, [1024]] # 10
  29. # YOLOv8.0-p2 head
  30. head:
  31.   - [-1, 1, nn.Upsample, [None, 2, "nearest"]]
  32.   - [[-1, 6], 1, Concat, [1]] # cat backbone P4
  33.   - [-1, 2, C3k2, [512, False]] # 13
  35.   - [-1, 1, nn.Upsample, [None, 2, "nearest"]]
  36.   - [[-1, 4], 1, Concat, [1]] # cat backbone P3
  37.   - [-1, 2, C3k2, [256, False]] # 16 (P3/8-small)
  39.   - [-1, 1, nn.Upsample, [None, 2, "nearest"]]
  40.   - [[-1, 2], 1, Concat, [1]] # cat backbone P2
  41.   - [-1, 2, C3k2, [128, False]] # 19 (P2/4-xsmall)  # 小目标可以尝试将这里的False设置为True.
  43.   - [-1, 1, Conv, [128, 3, 2]]
  44.   - [[-1, 16], 1, Concat, [1]] # cat head P3
  45.   - [-1, 2, C3k2, [256, False]] # 22 (P3/8-small)
  47.   - [-1, 1, Conv, [256, 3, 2]]
  48.   - [[-1, 13], 1, Concat, [1]] # cat head P4
  49.   - [-1, 2, C3k2, [512, False]] # 25 (P4/16-medium)
  51.   - [-1, 1, Conv, [512, 3, 2]]
  52.   - [[-1, 10], 1, Concat, [1]] # cat head P5
  53.   - [-1, 2, C3k2, [1024, True]] # 28 (P5/32-large)
  55.   - [[19, 22, 25, 28], 1, Detect, [nc]] # Detect(P2, P3, P4, P5)

2.2 增加大目标检测层P6的yaml文件

此版本的训练信息：YOLO11-P6 summary: 385 layers, 3,691,148 parameters, 3,691,132 gradients, 5.1 GFLOPs 

  1. # Ultralytics YOLO 🚀, AGPL-3.0 license
  2. # YOLO11 object detection model with P3-P5 outputs. For Usage examples see https://docs.ultralytics.com/tasks/detect
  4. # Parameters
  5. nc: 80 # number of classes
  6. scales: # model compound scaling constants, i.e. 'model=yolo11n.yaml' will call yolo11.yaml with scale 'n'
  7.   # [depth, width, max_channels]
  8.   n: [0.50, 0.25, 1024] # summary: 319 layers, 2624080 parameters, 2624064 gradients, 6.6 GFLOPs
  9.   s: [0.50, 0.50, 1024] # summary: 319 layers, 9458752 parameters, 9458736 gradients, 21.7 GFLOPs
  10.   m: [0.50, 1.00, 512] # summary: 409 layers, 20114688 parameters, 20114672 gradients, 68.5 GFLOPs
  11.   l: [1.00, 1.00, 512] # summary: 631 layers, 25372160 parameters, 25372144 gradients, 87.6 GFLOPs
  12.   x: [1.00, 1.50, 512] # summary: 631 layers, 56966176 parameters, 56966160 gradients, 196.0 GFLOPs
  14. # YOLO11n backbone
  15. backbone:
  16.   # [from, repeats, module, args]
  17.   - [-1, 1, Conv, [64, 3, 2]] # 0-P1/2
  18.   - [-1, 1, Conv, [128, 3, 2]] # 1-P2/4
  19.   - [-1, 2, C3k2, [128, False, 0.25]]
  20.   - [-1, 1, Conv, [256, 3, 2]] # 3-P3/8
  21.   - [-1, 2, C3k2, [256, False, 0.25]]
  22.   - [-1, 1, Conv, [512, 3, 2]] # 5-P4/16
  23.   - [-1, 2, C3k2, [512, False, 0.25]]
  24.   - [-1, 1, Conv, [768, 3, 2]] # 7-P5/32
  25.   - [-1, 2, C3k2, [768, True]]
  26.   - [-1, 1, Conv, [1024, 3, 2]] # 9-P6/64
  27.   - [-1, 2, C3k2, [1024, True]]
  28.   - [-1, 1, SPPF, [1024, 5]] # 11
  30. # YOLOv11.0x6 head
  31. head:
  32.   - [-1, 1, nn.Upsample, [None, 2, "nearest"]]
  33.   - [[-1, 8], 1, Concat, [1]] # cat backbone P5
  34.   - [-1, 2, C3k2, [768, False]] # 14
  36.   - [-1, 1, nn.Upsample, [None, 2, "nearest"]]
  37.   - [[-1, 6], 1, Concat, [1]] # cat backbone P4
  38.   - [-1, 2, C3k2, [512, False]] # 17
  40.   - [-1, 1, nn.Upsample, [None, 2, "nearest"]]
  41.   - [[-1, 4], 1, Concat, [1]] # cat backbone P3
  42.   - [-1, 2, C3k2, [256, False]] # 20 (P3/8-small)
  44.   - [-1, 1, Conv, [256, 3, 2]]
  45.   - [[-1, 17], 1, Concat, [1]] # cat head P4
  46.   - [-1, 2, C3k2, [512, False]] # 23 (P4/16-medium)
  48.   - [-1, 1, Conv, [512, 3, 2]]
  49.   - [[-1, 14], 1, Concat, [1]] # cat head P5
  50.   - [-1, 2, C3k2, [768, True]] # 26 (P5/32-large)
  52.   - [-1, 1, Conv, [768, 3, 2]]
  53.   - [[-1, 11], 1, Concat, [1]] # cat head P6
  54.   - [-1, 2, C3k2, [1024, True]] # 29 (P6/64-xlarge)  # True也可设置False尝试.
  56.   - [[20, 23, 26, 29], 1, Detect, [nc]] # Detect(P3, P4, P5, P6)

2.3 训练代码

大家可以创建一个py文件将我给的代码 复制粘贴 进去，配置好自己的文件路径即可运行。 

  1. import warnings
  2. warnings.filterwarnings('ignore')
  3. from ultralytics import YOLO
  5. if __name__ == '__main__':
  6.     model = YOLO('yolov8-MLLA.yaml')
  7.     # 如何切换模型版本, 上面的ymal文件可以改为 yolov8s.yaml就是使用的v8s,
  8.     # 类似某个改进的yaml文件名称为yolov8-XXX.yaml那么如果想使用其它版本就把上面的名称改为yolov8l-XXX.yaml即可（改的是上面YOLO中间的名字不是配置文件的）！
  9.     # model.load('yolov8n.pt') # 是否加载预训练权重,科研不建议大家加载否则很难提升精度
  10.     model.train(data=r"C:\Users\Administrator\PycharmProjects\yolov5-master\yolov5-master\Construction Site Safety.v30-raw-images_latestversion.yolov8\data.yaml",
  11.                 # 如果大家任务是其它的'ultralytics/cfg/default.yaml'找到这里修改task可以改成detect, segment, classify, pose
  12.                 cache=False,
  13.                 imgsz=640,
  14.                 epochs=150,
  15.                 single_cls=False,  # 是否是单类别检测
  16.                 batch=16,
  17.                 close_mosaic=0,
  18.                 workers=0,
  19.                 device='0',
  20.                 optimizer='SGD', # using SGD
  21.                 # resume='runs/train/exp21/weights/last.pt', # 如过想续训就设置last.pt的地址
  22.                 amp=False,  # 如果出现训练损失为Nan可以关闭amp
  23.                 project='runs/train',
  24.                 name='exp',
  25.                 )

2.4 训练过程截图

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三、本文总结

到此本文的正式分享内容就结束了，在这里给大家推荐我的YOLOv11改进有效涨点专栏，本专栏目前为新开的平均质量分98分，后期我会根据各种最新的前沿顶会进行论文复现，也会对一些老的改进机制进行补充，如果大家觉得本文帮助到你了，订阅本专栏，关注后续更多的更新~